Class lectures: Tuesdays 12:35-2:25pm
in Eng253, Mudd

Driven by rapid advances in many fields such as Biology, Finance and
Web Services, applications involving millions or even billions of data
items such as documents, user records, reviews, images or videos are
not that uncommon. Can we develop methods that can learn efficiently
from these massive amounts of potentially noisy data? There is an
urgent need to revisit the traditional machine learning methods and
tools to bridge the wide gap between large scale practical
requirements and traditional learning approaches.

The goal of this course is to introduce fundamental concepts of
large-scale machine learning. Both theoretical and practical aspects
will be discussed. The primary focus of the course will be on
analyzing basic tools of large-scale learning including the relevant
theory and algorithms rather than focusing on specific machine
learning techniques. It will also provide running examples from
real-world settings from various fields including Vision and
Information Retrieval. The course will prepare students to evolve a
new dimension while developing models and optimization techniques to
solve a practical problem - scalability.

We will analyze tools for large-scale learning that can be applied to
a variety of commonly used machine learning techniques for
classification, regression, ranking, clustering, density estimation
and semi-supervised learning. Example applications of these tools to
specific learning methods will also be provided. A tentative list of
tools we plan to discuss is given below:

Randomized Algorithms

Matrix Approximations I (low-rank approximation, decomposition)

Matrix Approximations II (sparse matrices, matrix completion)

Approximate Nearest Neighbor Search I (trees)

Approximate Nearest Neighbor Search II (hashes)

Fast Optimization (first-order methods)

Kernel Methods I (fast training)

Kernel Methods II (fast testing)

Dimensionality Reduction (linear and nonlinear methods)

Sparse Methods/Streaming (sparse coding...)

Announcements

Please check this section frequently for new announcements.

The slides for "Sparse Methods" are now available under Lectures. (Nov 22)

The slides for "Dimensionality Reduction" are now available under Lectures. (Nov 16)

Assignment 3 is due end of the day today. (Nov 16)

The slides for "Kernel Methods II" are now available under Lectures. (Nov 8)